The number of other artifacts that each artifact \(i\)  interfaces with is called the degree \(k_i\)  of \(i\) . A normalized histogram of a network's degrees is called a degree distribution [1]}. Ample studies have shown that artifacts in many (but not all) complex systems follow a scale-free degree distribution, also called power-law or inverse exponential distributions [2]}, [3]}, [4]}, [5]}, [6]}, [7]}, [8]}. Scale-free distributions take the form \(p_k=c_1 k^{-c_2}\)  where \(p_k\)  is the probability of randomly selecting a node with degree \(k\) , \(c_1\)  is a constant, and positive constant \(c_2\)  is the exponent of the power law with typical values of \(2\le c_2\le 3\)  [1]}. The resulting function appears as a negatively-sloping line in a log-log plot as in Fig. REF . Braha & Bar-Yam [5]}, [6]} and Sosa et al. [8]} suggest that complex system degree distributions generally follow a power-law with a cut-off at some large degree reminiscent of a bow-tie structure, making systems resilient to most small perturbations but vulnerable to certain rare perturbations [13]}, [14]}. Consequently, scale-free and bow-tie distributions may each provide unique insights.
